In recent times, a plethora of Large Code Generation Models (LCGMs) have been
proposed, showcasing significant potential in assisting developers with complex
programming tasks. Benchmarking LCGMs necessitates the creation of a set of
diverse programming problems, and each problem comprises the prompt (including
the task description), canonical solution, and test inputs. The existing
methods for constructing such a problem set can be categorized into two main
types: manual methods and perturbation-based methods. However, manual methods
demand high effort and lack scalability, while also risking data integrity due
to LCGMs' potentially contaminated data collection, and perturbation-based
approaches mainly generate semantically homogeneous problems with the same
canonical solutions and introduce typos that can be easily auto-corrected by
IDE, making them ineffective and unrealistic. In this work, we propose the idea
of programming problem merging (PPM) and provide two implementation of this
idea, we utilize our tool on two widely-used datasets and compare it against
nine baseline methods using eight code generation models. The results
demonstrate the effectiveness of our tool in generating more challenging,
diverse, and natural programming problems, comparing to the baselines.

我们提出了编程问题合并（PPM）的概念，并提供了两种实现方法，我们使用我们的工具在两个广泛使用的数据集上，并使用八个代码生成模型将其与九种基准方法进行了比较，结果表明，与基准相比，我们的工具在生成更具挑战性、多样化和自然的编程问题方面更具有效性。